OpenRaiser/Intern-Atlas huggingface dataset:
Quantifying Permission-Creep in the Google Play Store
Venue: arXiv cs.CR 2016
Year: 2016

Although there are over 1,600,000 third-party Android apps in the Google Play Store, little has been conclusively shown about how their individual (and collective) permission usage has evolved over time. Recently, Android 6 overhauled the way permissions are granted by users, by switching to run-time permission requests instead of install-time permission requests. This is a welcome change, but recent research has shown that many users continue to accept run-time permissions blindly, leaving them at the mercy of third-party app developers and adversaries. Beyond intentionally invading privacy, highly privileged apps increase the attack surface of smartphones and are more attractive targets for adversaries. This work focuses exclusively on dangerous permissions, i.e., those permissions identified by Android as guarding access to sensitive user data. By taking snapshots of the Google Play Store over a 20-month period, we characterise changes in the number and type of dangerous permissions used by Android apps when they are updated, to gain a greater understanding of the evolution of permission usage. We found that approximately 25,000 apps asked for additional permissions every three months. Worryingly, we made statistically significant observations that free apps and highly popular apps were more likely to ask for additional permissions when they were updated. By looking at patterns in dangerous permission usage, we find evidence that suggests developers may still be failing to correctly specify the permissions their apps need.
Deep neural networks are robust to weight binarization and other non-linear distortions
Venue: arXiv cs.NE 2016
Year: 2016

Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we show that this is just the tip of the iceberg: these same networks, during testing, also exhibit a remarkable robustness to distortions beyond quantization, including additive and multiplicative noise, and a class of non-linear projections where binarization is just a special case. To quantify this robustness, we show that one such network achieves 11% test error on CIFAR-10 even with 0.68 effective bits per weight. Furthermore, we find that a common training heuristic--namely, projecting quantized weights during backpropagation--can be altered (or even removed) and networks still achieve a base level of robustness during testing. Specifically, training with weight projections other than quantization also works, as does simply clipping the weights, both of which have never been reported before. We confirm our results for CIFAR-10 and ImageNet datasets. Finally, drawing from these ideas, we propose a stochastic projection rule that leads to a new state of the art network with 7.64% test error on CIFAR-10 using no data augmentation.
Big Data Refinement
Venue: arXiv cs.OH 2016
Year: 2016

"Big data" has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil refinery, extracting the essence out of the raw data. Pessimists look for their imagery to the other end of the petrol cycle, and talk about the "data exhausts" of our society. Obviously, the refinement community knows how to do "refining". This paper explores the extent to which notions of refinement and data in the formal methods community relate to the core concepts in "big data". In particular, can the data refinement paradigm can be used to explain aspects of big data processing?
Towards linking correctness conditions for concurrent objects and contextual trace refinement
Venue: arXiv cs.LO 2016
Year: 2016

Correctness conditions for concurrent objects describe how atomicity of an abstract sequential object may be decomposed. Many different concurrent objects and proof methods for them have been developed. However, arguments about correctness are conducted with respect to an object in isolation. This is in contrast to real-world practice, where concurrent objects are often implemented as part of a programming language library (e.g., java.util.concurrent) and are instantiated within a client program. A natural question to ask, then is: How does a correctness condition for a concurrent object ensure correctness of a client program that uses the concurrent object? This paper presents the main issues that surround this question and provides some answers by linking different correctness conditions with a form of trace refinement.
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Venue: arXiv cs.CV 2016
Year: 2016

Detecting hand actions from ego-centric depth sequences is a practically challenging problem, owing mostly to the complex and dexterous nature of hand articulations as well as non-stationary camera motion. We address this problem via a Hough transform based approach coupled with a discriminatively learned error-correcting component to tackle the well known issue of incorrect votes from the Hough transform. In this framework, local parts vote collectively for the start $\&$ end positions of each action over time. We also construct an in-house annotated dataset of 300 long videos, containing 3,177 single-action subsequences over 16 action classes collected from 26 individuals. Our system is empirically evaluated on this real-life dataset for both the action recognition and detection tasks, and is shown to produce satisfactory results. To facilitate reproduction, the new dataset and our implementation are also provided online.
A Distributed Force-Directed Algorithm on Giraph: Design and Experiments
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we study the problem of designing a distributed graph visualization algorithm for large graphs. The algorithm must be simple to implement and the computing infrastructure must not require major hardware or software investments. We design, implement, and experiment a force-directed algorithm in Giraph, a popular open source framework for distributed computing, based on a vertex-centric design paradigm. The algorithm is tested both on real and artificial graphs with up to million edges, by using a rather inexpensive PaaS (Platform as a Service) infrastructure of Amazon. The experiments show the scalability and effectiveness of our technique when compared to a centralized implementation of the same force-directed model. We show that graphs with about one million edges can be drawn in less than 8 minutes, by spending about 1\$ per drawing in the cloud computing infrastructure.
Initialization Errors in Quantum Data Base Recall
Venue: arXiv cs.DB 2016
Year: 2016

This paper analyzes the relationship between initialization error and recall of a specific memory in the Grover algorithm for quantum database search. It is shown that the correct memory is obtained with high probability even when the initial state is far removed from the correct one. The analysis is done by relating the variance of error in the initial state to the recovery of the correct memory and the surprising result is obtained that the relationship between the two is essentially linear.
Energy-Aware Relay Selection and Power Allocation for Multiple-User Cooperative Networks
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates the relay assignment and power allocation problem for two different network power management policies: group lifetime maximization (GLM) and minimum weighted total power (MWTP), with the aim of lifetime maximization in symbol error rate (SER) constrained multipleuser cooperative network. With optimal power allocation solution obtained for each policy, we show that the optimal relay assignment can be obtained using bottleneck matching (BM) algorithm and minimum weighted matching (MWM) algorithm for GLM and MWTP policies, respectively. Since relay assignment with BM algorithm is not power efficient, we propose a novel minimum bottleneck matching (MBM) algorithm to solve the relay assignment problem optimally for GLM policy. To further reduce the complexity of the relay assignment, we propose suboptimal relay selection (SRS) algorithm which has linear complexity in number of source and relay nodes. Simulation results demonstrate that the proposed relay selection and power allocation strategies based on GLM policy have better network lifetime performance over the strategies based on MWTP policy. Compared to the MBM and SRS algorithm, relay assignment based on BM algorithm for GLM policy has inferior network lifetime performance at low update interval. We show that relay assignment based on MBM algorithm achieves maximum network lifetime performance and relay assignment with SRS algorithm has performance very close to it.
Non-aligned drawings of planar graphs
Venue: arXiv cs.CG 2016
Year: 2016

A non-aligned drawing of a graph is a drawing where no two vertices are in the same row or column. Auber et al. showed that not all planar graphs have non-aligned drawings that are straight-line, planar, and in the minimal-possible $n\times n$-grid. They also showed that such drawings exist if up to $n-3$ edges may have a bend. In this paper, we give algorithms for non-aligned planar drawings that improve on the results by Auber et al. In particular, we give such drawings in an $n\times n$-grid with significantly fewer bends, and we study what grid-size can be achieved if we insist on having straight-line drawings.
Multi-resource defensive strategies for patrolling games with alarm systems
Venue: arXiv cs.AI 2016
Year: 2016

Security Games employ game theoretical tools to derive resource allocation strategies in security domains. Recent works considered the presence of alarm systems, even suffering various forms of uncertainty, and showed that disregarding alarm signals may lead to arbitrarily bad strategies. The central problem with an alarm system, unexplored in other Security Games, is finding the best strategy to respond to alarm signals for each mobile defensive resource. The literature provides results for the basic single-resource case, showing that even in that case the problem is computationally hard. In this paper, we focus on the challenging problem of designing algorithms scaling with multiple resources. First, we focus on finding the minimum number of resources assuring non-null protection to every target. Then, we deal with the computation of multi-resource strategies with different degrees of coordination among resources. For each considered problem, we provide a computational analysis and propose algorithmic methods.
Emotional Intensity analysis in Bipolar subjects
Venue: arXiv cs.AI 2016
Year: 2016

The massive availability of digital repositories of human thought opens radical novel way of studying the human mind. Natural language processing tools and computational models have evolved such that many mental conditions are predicted by analysing speech. Transcription of interviews and discourses are analyzed using syntactic, grammatical or sentiment analysis to infer the mental state. Here we set to investigate if classification of Bipolar and control subjects is possible. We develop the Emotion Intensity Index based on the Dictionary of Affect, and find that subjects categories are distinguishable. Using classical classification techniques we get more than 75\% of labeling performance. These results sumed to previous studies show that current automated speech analysis is capable of identifying altered mental states towards a quantitative psychiatry.
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pretrained CNN image recognition model for video segmentation task can severely hurt performance. We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data. By explicitly modeling and compensating for the domain shift from the source domain to the target domain, this proposed approach underpins a robust semantic object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
Venue: arXiv cs.CV 2016
Year: 2016

It is a challenge for Phase Measurement Profilometry (PMP) to measure objects with a large range of reflectivity variation across the surface. Saturated or dark pixels in the deformed fringe patterns captured by the camera will lead to phase fluctuations and errors. Jiang et al. proposed a high dynamic range real-time 3D shape measurement method without changing camera exposures. Three inverted phase-shifted fringe patterns are used to complement three regular phase-shifted fringe patterns for phase retrieval when any of the regular fringe patterns are saturated. But Jiang's method still has some drawbacks: (1) The phases in saturated pixels are respectively estimated by different formulas for different cases. It is shortage of an universal formula; (2) it cannot be extended to four-step phase-shifting algorithm because inverted fringe patterns are the repetition of regular fringe patterns; (3) only three unsaturated intensity values at every pixel of fringe patterns are chosen for phase demodulation, lying idle the other unsaturated ones. We proposed a method for enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm, which combines the complementary technique of inverted and regular fringe patterns with generalized phase-shifting algorithm. Firstly, two sets of complementary phase-shifted fringe patterns, namely regular and inverted fringe patterns are projected and collected. Then all unsaturated intensity values at the same camera pixel from two sets of fringe patterns are selected, and employed to retrieve the phase by generalized phase-shifting algorithm. Finally, simulations and experiments are conducted to prove the validity of the proposed method. The results are analyzed and compared with Jiang's method, which demonstrate that the proposed method not only expands the scope of Jiang's method, but also improves the measurement accuracy.
High Capacity Image Steganography using Adjunctive Numerical Representations with Multiple Bit-Plane Decomposition Methods
Venue: arXiv cs.MM 2016
Year: 2016

LSB steganography is a one of the most widely used methods for implementing covert data channels in image file exchanges [1][2]. The low computational complexity and implementation simplicity of the algorithm are significant factors for its popularity with the primary reason being low image distortion. Many attempts have been made to increase the embedding capacity of LSB algorithms by expanding into the second or third binary layers of the image while maintaining a low probability of detection with minimal distortive effects [2][3][4]. In this paper, we introduce an advanced technique for covertly embedding data within images using redundant number system decomposition over non-standard digital bit planes. Both grayscale and bit-mapped images are equally effective as cover files. It will be shown that this unique steganography method has minimal visual distortive affects while also preserving the cover file statistics, making it less susceptible to most general steganography detection algorithms.
Decentralized AP Selection in Large-Scale Wireless LANs Considering Multi-AP Interference
Venue: arXiv cs.NI 2016
Year: 2016

Densification of access points (APs) in wireless local area networks (WLANs) increases the interference and the contention domains of each AP due to multiple overlapped basic service sets (BSSs). Consequently, high interference from multiple co-channel BSS at the target AP impairs system performance. To improve system performance in the presence of multi-BSSs interference, we propose a decentralized AP selection scheme that takes interference at the candidate APs into account and selects AP that offers best signal-interference-plus noise ratio (SINR). In the proposed algorithm, the AP selection process is distributed at the user stations (STAs) and is based on the estimated SINR in the downlink. Estimating SINR in the downlink helps capture the effect of interference from neighboring BSSs or APs. Based on a simulated large-scale 802.11 network, the proposed scheme outperforms the strongest signal first (SSF) AP selection scheme used in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [3]; it achieves 99% and 43% gains in aggregate throughput over SSF and MPD, respectively. While increasing STA densification, the proposed scheme is shown to increase aggregate network performance.
Energy Efficiency in Multicast Multihop D2D Networks
Venue: arXiv cs.IT 2016
Year: 2016

As the demand of mobile devices (MDs) for data services is explosively increasing, traditional offloading in the cellular networks is facing the contradiction of energy efficiency and quality of service. Device-to-device (D2D) communication is considered as an effective solution. This work investigates a scenario where the MDs have the same demand for common content and they cooperate to deliver it using multicast multihop relaying. We focus on the problem of total power minimization by grouping the MDs in multihop D2D networks, while maintaining the minimum rate requirement of each MD. As the problem is shown to be NP-complete and the optimal solution can not be found efficiently, two greedy algorithms are proposed to solve this problem in polynomial time. Simulation results demonstrate that lots of power can be saved in the content delivery situation using multihop D2D communication, and the proposed algorithms are suitable for different situations with different advantages.
Energy-Efficient Transmission Design in Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2016
Year: 2016

Non-orthogonal multiple access (NOMA) is considered as a promising technology for improving the spectral efficiency (SE) in 5G. In this correspondence, we study the benefit of NOMA in enhancing energy efficiency (EE) for a multi-user downlink transmission, where the EE is defined as the ratio of the achievable sum rate of the users to the total power consumption. Our goal is to maximize the EE subject to a minimum required data rate for each user, which leads to a non-convex fractional programming problem. To solve it, we first establish the feasible range of the transmitting power that is able to support each user's data rate requirement. Then, we propose an EE-optimal power allocation strategy that maximizes the EE. Our numerical results show that NOMA has superior EE performance in comparison with conventional orthogonal multiple access (OMA).
VIF: Virtual Interactive Fiction (with a twist)
Venue: arXiv cs.HC 2016
Year: 2016

Nowadays computer science can create digital worlds that deeply immerse users; it can also process in real time brain activity to infer their inner states. What marvels can we achieve with such technologies? Go back to displaying text. And unfold a story that follows and molds users as never before.
Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications
Venue: arXiv cs.HC 2016
Year: 2016

Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applications , however the dissemination of the technology is limited, in part because of the price of the hardware. In this paper we compare side by side two EEG amplifiers, the consumer grade OpenBCI and the medical grade g.tec g.USBamp. For this purpose, we employed an original montage, based on the simultaneous recording of the same set of electrodes. Two set of recordings were performed. During the first experiment a simple adapter with a direct connection between the amplifiers and the electrodes was used. Then, in a second experiment, we attempted to discard any possible interference that one amplifier could cause to the other by adding "ideal" diodes to the adapter. Both spectral and temporal features were tested -- the former with a workload monitoring task, the latter with an visual P300 speller task. Overall, the results suggest that the OpenBCI board -- or a similar solution based on the Texas Instrument ADS1299 chip -- could be an effective alternative to traditional EEG devices. Even though a medical grade equipment still outperforms the OpenBCI, the latter gives very close EEG readings, resulting in practice in a classification accuracy that may be suitable for popularizing BCI uses.
On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets
Venue: arXiv cs.CL 2016
Year: 2016

Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.
Using Design Science to build a Watermark System for Righful Ownership Protection in the Cloud
Venue: arXiv cs.CY 2016
Year: 2016

Cloud computing opportunities have presented service options for users that are both economical and flexible to use requirements. However, the risk analysis for the user identifies vulnerabilities for intellectual property ownership and vulnerabilities for the identification of rightful property owners when cloud services are used. It is common for image owners to embed watermarks and other security mechanisms into their property so that the rightful ownership may be identified. In this paper we present a design that overcomes many of the current limitations in cloud watermarking uses and propose a schema that places responsibility on the cloud provider to have a robust information protection program. Such a design solution lays out an information security architecture that enhances utility for cloud services and gives better options for users to securely place properties in the cloud. The Design Science methodology is used to build the artefact.